Statistics for Business & Economics, 14th Edition
David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann
Contents
ABOUT THE AUTHORS xxi
PREFACE xxv
Chapter 1 Data and Statistics 1
Statistics in Practice: Bloomberg Business week 2
1.1 Applications in Business and Economics 3
Accounting 3
Finance 3
Production 4
Economics 4
Information Systems 4
1.2 Data 5
Elements, Variables, and Observations 5
Scales of Measurement 5
Categorical and Quantitative Data 7
Cross-Sectional and Time Series Data 8
1.3 Data Sources 10
Existing Sources 10
Observational Study 11
Experiment 12
Time and Cost Issues 13
Data Acquisition Errors 13
1.4 Descriptive Statistics 13
1.5 Statistical Inference 15
1.6 Analytics 16
1.7 Big Data and Data Mining 17
1.8 Computers and Statistical Analysis 19
1.9 Ethical Guidelines for Statistical Practice 19
Summary 21
Glossary 21
Supplementary Exercises 22
Appendix 1.1 Opening and Saving DATA Files and Converting to Stacked
form with JMP 30
Appendix 1.2 Getting Started with R and RStudio (MindTap Reader)
Appendix 1.3 Basic Data Manipulation in R (MindTap Reader)
Chapter 2 D escriptive Statistics: Tabular and Graphical
Displays 33
Statistics in Practice: Colgate-Palmolive Company 34
2.1 Summarizing Data for a Categorical Variable 35
Frequency Distribution 35
Relative Frequency and Percent Frequency Distributions 36
Bar Charts and Pie Charts 37
2.2 Summarizing Data for a Quantitative Variable 42
Frequency Distribution 42
Relative Frequency and Percent Frequency Distributions 44
Dot Plot 45
Histogram 45
Cumulative Distributions 47
Stem-and-Leaf Display 47
2.3 Summarizing Data for Two Variables Using Tables 57
Crosstabulation 57
Simpson’s Paradox 59
2.4 Summarizing Data for Two Variables Using Graphical
Displays 65
Scatter Diagram and Trendline 65
Side-by-Side and Stacked Bar Charts 66
2.5 Data Visualization: Best Practices in Creating Effective
Graphical Displays 71
Creating Effective Graphical Displays 71
Choosing the Type of Graphical Display 72
Data Dashboards 73
Data Visualization in Practice: Cincinnati Zoo
and Botanical Garden 75
Summary 77
Glossary 78
Key Formulas 79
Supplementary Exercises 80
Case Problem 1: Pelican Stores 85
Case Problem 2: Movie Theater Releases 86
Case Problem 3: Queen City 87
Case Problem 4: Cut-Rate Machining, Inc. 88
Appendix 2.1 Creating Tabular and Graphical Presentations with JMP 90
Appendix 2.2 Creating Tabular and Graphical Presentations
with Excel 93
Appendix 2.3 Creating Tabular and Graphical Presentations with R
(MindTap Reader)
Chapter 3 Descriptive Statistics: Numerical Measures 107
Statistics in Practice: Small Fry Design 108
3.1 Measures of Location 109
Mean 109
Weighted Mean 111
Median 112
Geometric Mean 113
Mode 115
Percentiles 115
Quartiles 116
3.2 Measures of Variability 122
Range 123
Interquartile Range 123
Variance 123
Standard Deviation 125
Coefficient of Variation 126
3.3 Measures of Distribution Shape, Relative Location,
and Detecting Outliers 129
Distribution Shape 129
z-Scores 130
Chebyshev’s Theorem 131
Empirical Rule 132
Detecting Outliers 134
3.4 Five-Number Summaries and Boxplots 137
Five-Number Summary 138
Boxplot 138
Comparative Analysis Using Boxplots 139
3.5 Measures of Association Between Two Variables 142
Covariance 142
Interpretation of the Covariance 144
Correlation Coefficient 146
Interpretation of the Correlation Coefficient 147
3.6 Data Dashboards: Adding Numerical Measures to
Improve Effectiveness 150
Summary 153
Glossary 154
Key Formulas 155
Supplementary Exercises 156
Case Problem 1: Pelican Stores 162
Case Problem 2: Movie Theater Releases 163
Case Problem 3: Business Schools of Asia-Pacific 164
Case Problem 4: Heavenly Chocolates Website Transactions 164
Case Problem 5: African Elephant Populations 166
Appendix 3.1 Descriptive Statistics with JMP 168
Appendix 3.2 Descriptive Statistics with Excel 171
Appendix 3.3 Descriptive Statistics with R (MindTap Reader)
Chapter 4 Introduction to Probability 177
Statistics in Practice: National Aeronautics and Space
Administration 178
4.1 Random Experiments, Counting Rules,
and Assigning Probabilities 179
Counting Rules, Combinations, and Permutations 180
Assigning Probabilities 184
Probabilities for the KP&L Project 185
4.2 Events and Their Probabilities 189
4.3 Some Basic Relationships of Probability 193
Complement of an Event 193
Addition Law 194
4.4 Conditional Probability 199
Independent Events 202
Multiplication Law 202
4.5 Bayes’ Theorem 207
Tabular Approach 210
Summary 212
Glossary 213
Key Formulas 214
Supplementary Exercises 214
Case Problem 1: Hamilton County Judges 219
Case Problem 2: Rob’s Market 221
Chapter 5 Discrete Probability Distributions 223
Statistics in Practice: Voter Waiting Times in Elections 224
5.1 Random Variables 225
Discrete Random Variables 225
Continuous Random Variables 225
5.2 Developing Discrete Probability Distributions 228
5.3 Expected Value and Variance 233
Expected Value 233
Variance 233
5.4 Bivariate Distributions, Covariance, and Financial Portfolios 238
A Bivariate Empirical Discrete Probability Distribution 238
Financial Applications 241
Summary 244
5.5 Binomial Probability Distribution 247
A Binomial Experiment 248
Martin Clothing Store Problem 249
Using Tables of Binomial Probabilities 253
Expected Value and Variance for the Binomial Distribution 254
5.6 Poisson Probability Distribution 258
An Example Involving Time Intervals 259
An Example Involving Length or Distance Intervals 260
5.7 Hypergeometric Probability Distribution 262
Summary 265
Glossary 266
Key Formulas 266
Supplementary Exercises 268
Case Problem 1: Go Bananas! Breakfast Cereal 272
Case Problem 2: McNeil’s Auto Mall 272
Case Problem 3: Grievance Committee at Tuglar Corporation 273
Appendix 5.1 Discrete Probability Distributions with JMP 275
Appendix 5.2 Discrete Probability Distributions with Excel 278
Appendix 5.3 Discrete Probability Distributions with R (MindTap Reader)
Chapter 6 Continuous Probability Distributions 281
Statistics in Practice: Procter & Gamble 282
6.1 Uniform Probability Distribution 283
Area as a Measure of Probability 284
6.2 Normal Probability Distribution 287
Normal Curve 287
Standard Normal Probability Distribution 289
Computing Probabilities for Any Normal Probability
Distribution 294
Grear Tire Company Problem 294
6.3 Normal Approximation of Binomial Probabilities 299
6.4 Exponential Probability Distribution 302
Computing Probabilities for the Exponential
Distribution 302
Relationship Between the Poisson and Exponential
Distributions 303
Summary 305
Glossary 305
Key Formulas 306
Supplementary Exercises 306
Case Problem 1: Specialty Toys 309
Case Problem 2: Gebhardt Electronics 311
Appendix 6.1 Continuous Probability Distributions with JMP 312
Appendix 6.2 Continuous Probability Distributions with Excel 317
Appendix 6.3 Continuous Probability Distribution with R
(MindTap Reader)
Chapter 7 Sampling and Sampling Distributions 319
Statistics in Practice: Meadwestvaco Corporation 320
7.1 The Electronics Associates Sampling Problem 321
7.2 Selecting a Sample 322
Sampling from a Finite Population 322
Sampling from an Infinite Population 324
7.3 Point Estimation 327
Practical Advice 329
7.4 Introduction to Sampling Distributions 331
7.5 Sampling Distribution of x 333
Expected Value of x 334
Standard Deviation of x 334
Form of the Sampling Distribution of x 335
Sampling Distribution of x for the EAI Problem 337
Practical Value of the Sampling Distribution of x 338
Relationship Between the Sample Size and the Sampling
Distribution of x 339
7.6 Sampling Distribution of p 343
Expected Value of p 344
Standard Deviation of p 344
Form of the Sampling Distribution of p 345
Practical Value of the Sampling Distribution of p 345
7.7 Properties of Point Estimators 349
Unbiased 349
Efficiency 350
Consistency 351
7.8 Other Sampling Methods 351
Stratified Random Sampling 352
Cluster Sampling 352
Systematic Sampling 353
Convenience Sampling 353
Judgment Sampling 354
7.9 Big Data and Standard Errors of Sampling Distributions 354
Sampling Error 354
Nonsampling Error 355
Big Data 356
Understanding What Big Data Is 356
Implications of Big Data for Sampling Error 357
Summary 360
Glossary 361
Key Formulas 362
Supplementary Exercises 363
Case Problem: Marion Dairies 366
Appendix 7.1 The Expected Value and Standard Deviation of x 367
Appendix 7.2 Random Sampling with JMP 368
Appendix 7.3 Random Sampling with Excel 371
Appendix 7.4 Random Sampling with R (MindTap Reader)
Chapter 8 Interval Estimation 373
Statistics in Practice: Food Lion 374
8.1 Population Mean: s Known 375
Margin of Error and the Interval Estimate 375
Practical Advice 379
8.2 Population Mean: s Unknown 381
Margin of Error and the Interval Estimate 382
Practical Advice 385
Using a Small Sample 385
Summary of Interval Estimation Procedures 386
8.3 Determining the Sample Size 390
8.4 Population Proportion 393
Determining the Sample Size 394
8.5 Big Data and Confidence Intervals 398
Big Data and the Precision of Confidence Intervals 398
Implications of Big Data for Confidence Intervals 399
Summary 401
Glossary 402
Key Formulas 402
Supplementary Exercises 403
Case Problem 1: Young Professional Magazine 406
Case Problem 2: Gulf Real Estate Properties 407
Case Problem 3: Metropolitan Research, Inc. 409
Appendix 8.1 Interval Estimation with JMP 410
Appendix 8.2 Interval Estimation Using Excel 413
Appendix 8.3 Interval Estimation with R (MindTap Reader)
Chapter 9 Hypothesis Tests 417
Statistics in Practice: John Morrell & Company 418
9.1 Developing Null and Alternative Hypotheses 419
The Alternative Hypothesis as a Research Hypothesis 419
The Null Hypothesis as an Assumption to Be Challenged 420
Summary of Forms for Null and Alternative Hypotheses 421
9.2 Type I and Type II Errors 422
9.3 Population Mean: s Known 425
One-Tailed Test 425
Two-Tailed Test 430
Summary and Practical Advice 433
Relationship Between Interval Estimation and
Hypothesis Testing 434
9.4 Population Mean: s Unknown 439
One-Tailed Test 439
Two-Tailed Test 440
Summary and Practical Advice 441
9.5 Population Proportion 445
Summary 447
9.6 Hypothesis Testing and Decision Making 450
9.7 Calculating the Probability of Type II Errors 450
9.8 Determining the Sample Size for a Hypothesis Test
About a Population Mean 455
9.9 Big Data and Hypothesis Testing 459
Big Data, Hypothesis Testing, and p Values 459
Implications of Big Data in Hypothesis Testing 460
Summary 462
Glossary 462
Key Formulas 463
Supplementary Exercises 463
Case Problem 1: Quality Associates, Inc. 467
Case Problem 2: Ethical Behavior of Business Students
at Bayview University 469
Appendix 9.1 Hypothesis Testing with JMP 471
Appendix 9.2 Hypothesis Testing with Excel 475
Appendix 9.3 Hypothesis Testing with R (MindTap Reader)
Chapter 10 Inference About Means and Proportions with
Two Populations 481
Statistics in Practice: U.S. Food and Drug Administration 482
10.1 Inferences About the Difference Between Two
Population Means: s1 and s2 Known 483
Interval Estimation of m1 − m2 483
Hypothesis Tests About m1 − m2 485
Practical Advice 487
10.2 Inferences About the Difference Between Two
Population Means: s1 and s2 Unknown 489
Interval Estimation of m1 − m2 489
Hypothesis Tests About m1 − m2 491
Practical Advice 493
10.3 Inferences About the Difference Between Two
Population Means: Matched Samples 497
10.4 Inferences About the Difference Between Two Population
Proportions 503
Interval Estimation of p1 − p2 503
Hypothesis Tests About p1 − p2 505
Summary 509
Glossary 509
Key Formulas 509
Supplementary Exercises 511
Case Problem: Par, Inc. 514
Appendix 10.1 Inferences About Two Populations with JMP 515
Appendix 10.2 Inferences About Two Populations with Excel 519
Appendix 10.3 Inferences about Two Populations with R (MindTap Reader)
Chapter 11 Inferences About Population Variances 525
Statistics in Practice: U.S. Government Accountability Office 526
11.1 Inferences About a Population Variance 527
Interval Estimation 527
Hypothesis Testing 531
11.2 Inferences About Two Population Variances 537
Summary 544
Key Formulas 544
Supplementary Exercises 544
Case Problem 1: Air Force Training Program 546
Case Problem 2: Meticulous Drill & Reamer 547
Appendix 11.1 Population Variances with JMP 549
Appendix 11.2 Population Variances with Excel 551
Appendix 11.3 Population Variances with R (MindTap Reader)
Chapter 12 Comparing Multiple Proportions, Test of
Independence and Goodness of Fit 553
Statistics in Practice: United Way 554
12.1 Testing the Equality of Population Proportions
for Three or More Populations 555
A Multiple Comparison Procedure 560
12.2 Test of Independence 565
12.3 Goodness of Fit Test 573
Multinomial Probability Distribution 573
Normal Probability Distribution 576
Summary 582
Glossary 582
Key Formulas 583
Supplementary Exercises 583
Case Problem 1: A Bipartisan Agenda for Change 587
Case Problem 2: Fuentes Salty Snacks, Inc. 588
Case Problem 3: Fresno Board Games 588
Appendix 12.1 Chi-Square Tests with JMP 590
Appendix 12.2 Chi-Square Tests with Excel 593
Appendix 12.3 Chi-Squared Tests with R (MindTap Reader)
Chapter 13 E xperimental Design and Analysis
of Variance 597
Statistics in Practice: Burke Marketing Services, Inc. 598
13.1 An Introduction to Experimental Design
and Analysis of Variance 599
Data Collection 600
Assumptions for Analysis of Variance 601
Analysis of Variance: A Conceptual Overview 601
13.2 Analysis of Variance and the Completely
Randomized Design 604
Between-Treatments Estimate of Population Variance 605
Within-Treatments Estimate of Population Variance 606
Comparing the Variance Estimates: The F Test 606
ANOVA Table 608
Computer Results for Analysis of Variance 609
Testing for the Equality of k Population Means:
An Observational Study 610
13.3 Multiple Comparison Procedures 615
Fisher’s LSD 615
Type I Error Rates 617
13.4 Randomized Block Design 621
Air Traffic Controller Stress Test 621
ANOVA Procedure 623
Computations and Conclusions 623
13.5 Factorial Experiment 627
ANOVA Procedure 629
Computations and Conclusions 629
Summary 635
Glossary 635
Key Formulas 636
Supplementary Exercises 638
Case Problem 1: Wentworth Medical Center 643
Case Problem 2: Compensation for Sales Professionals 644
Case Problem 3: Touristopia Travel 644
Appendix 13.1 Analysis of Variance with JMP 646
Appendix 13.2 Analysis of Variance with Excel 649
Appendix 13.3 Analysis Variance with R (MindTap Reader)
Chapter 14 Simple Linear Regression 653
Statistics in Practice: Alliance Data Systems 654
14.1 Simple Linear Regression Model 655
Regression Model and Regression Equation 655
Estimated Regression Equation 656
14.2 Least Squares Method 658
14.3 Coefficient of Determination 668
Correlation Coefficient 671
14.4 Model Assumptions 675
14.5 Testing for Significance 676
Estimate of s2 676
t Test 677
Confidence Interval for b1 679
F Test 679
Some Cautions About the Interpretation of Significance Tests 681
14.6 Using the Estimated Regression Equation
for Estimation and Prediction 684
Interval Estimation 685
Confidence Interval for the Mean Value of y 685
Prediction Interval for an Individual Value of y 686
14.7 Computer Solution 691
14.8 Residual Analysis: Validating Model Assumptions 694
Residual Plot Against x 695
Residual Plot Against yˆ 697
Standardized Residuals 698
Normal Probability Plot 699
14.9 Residual Analysis: Outliers and Influential Observations 703
Detecting Outliers 703
Detecting Influential Observations 704
14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear
Regression 710
Summary 711
Glossary 711
Key Formulas 712
Supplementary Exercises 714
Case Problem 1: Measuring Stock Market Risk 721
Case Problem 2: U.S. Department of Transportation 721
Case Problem 3: Selecting a Point-and-Shoot Digital Camera 722
Case Problem 4: Finding the Best Car Value 723
Case Problem 5: Buckeye Creek Amusement Park 724
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas 726
Appendix 14.2 A Test for Significance Using Correlation 727
Appendix 14.3 Simple Linear Regression with JMP 727
Appendix 14.4 Regression Analysis with Excel 728
Appendix 14.5 Simple Linear Regression with R (MindTap Reader)
Chapter 15 Multiple Regression 731
Statistics in Practice: 84.51° 732
15.1 Multiple Regression Model 733
Regression Model and Regression Equation 733
Estimated Multiple Regression Equation 733
15.2 Least Squares Method 734
An Example: Butler Trucking Company 735
Note on Interpretation of Coefficients 737
15.3 Multiple Coefficient of Determination 743
15.4 Model Assumptions 746
15.5 Testing for Significance 747
F Test 747
t Test 750
Multicollinearity 750
15.6 Using the Estimated Regression Equation
for Estimation and Prediction 753
15.7 Categorical Independent Variables 755
An Example: Johnson Filtration, Inc. 756
Interpreting the Parameters 758
More Complex Categorical Variables 760
15.8 Residual Analysis 764
Detecting Outliers 766
Studentized Deleted Residuals and Outliers 766
Influential Observations 767
Using Cook’s Distance Measure to Identify
Influential Observations 767
15.9 Logistic Regression 771
Logistic Regression Equation 772
Estimating the Logistic Regression Equation 773
Testing for Significance 774
Managerial Use 775
Interpreting the Logistic Regression Equation 776
Logit Transformation 778
15.10 Practical Advice: Big Data and Hypothesis Testing
in Multiple Regression 782
Summary 783
Glossary 783
Key Formulas 784
Supplementary Exercises 786
Case Problem 1: Consumer Research, Inc. 790
Case Problem 2: Predicting Winnings for NASCAR Drivers 791
Case Problem 3: Finding the Best Car Value 792
Appendix 15.1 Multiple Linear Regression with JMP 794
Appendix 15.2 Logistic Regression with JMP 796
Appendix 15.3 Multiple Regression with Excel 797
Appendix 15.4 Multiple Linear Regression with R (MindTap Reader)
Appendix 15.5 Logistics Regression with R (MindTap Reader)
Chapter 16 Regression Analysis: Model Building 799
Statistics in Practice: Monsanto Company 800
16.1 General Linear Model 801
Modeling Curvilinear Relationships 801
Interaction 805
Transformations Involving the Dependent Variable 807
Nonlinear Models That Are Intrinsically Linear 812
16.2 Determining When to Add or Delete Variables 816
General Case 818
Use of p-Values 819
16.3 Analysis of a Larger Problem 822
16.4 Variable Selection Procedures 826
Stepwise Regression 826
Forward Selection 828
Backward Elimination 828
Best-Subsets Regression 828
Making the Final Choice 829
16.5 Multiple Regression Approach to Experimental Design 832
16.6 Autocorrelation and the Durbin-Watson Test 836
Summary 840
Glossary 841
Key Formulas 841
Supplementary Exercises 841
Case Problem 1: Analysis of LPGA Tour Statistics 845
Case Problem 2: Rating Wines from the Piedmont Region of Italy 846
Appendix 16.1 Variable Selection Procedures with JMP 848
Appendix 16.2 Variable Selection Procedures with R (MindTap Reader)
Chapter 17 Time Series Analysis and Forecasting 859
Statistics in Practice: Nevada Occupational Health Clinic 860
17.1 Time Series Patterns 861
Horizontal Pattern 861
Trend Pattern 863
Seasonal Pattern 863
Trend and Seasonal Pattern 864
Cyclical Pattern 864
Selecting a Forecasting Method 866
17.2 Forecast Accuracy 867
17.3 Moving Averages and Exponential Smoothing 872
Moving Averages 872
Weighted Moving Averages 874
Exponential Smoothing 875
17.4 Trend Projection 881
Linear Trend Regression 882
Nonlinear Trend Regression 886
17.5 Seasonality and Trend 891
Seasonality Without Trend 892
Seasonality and Trend 894
Models Based on Monthly Data 897
17.6 Time Series Decomposition 900
Calculating the Seasonal Indexes 902
Deseasonalizing the Time Series 905
Using the Deseasonalized Time Series to Identify Trend 905
Seasonal Adjustments 907
Models Based on Monthly Data 908
Cyclical Component 908
Summary 910
Glossary 911
Key Formulas 912
Supplementary Exercises 913
Case Problem 1: Forecasting Food and Beverage Sales 917
Case Problem 2: Forecasting Lost Sales 918
Appendix 17.1 Forecasting with JMP 920
Appendix 17.2 Forecasting with Excel 926
Appendix 17.3 Forecasting with R (MindTap Reader)
Chapter 18 Nonparametric Methods 931
Statistics in Practice: West Shell Realtors 932
18.1 Sign Test 933
Hypothesis Test About a Population Median 933
Hypothesis Test with Matched Samples 938
18.2 Wilcoxon Signed-Rank Test 941
18.3 Mann-Whitney-Wilcoxon Test 947
18.4 Kruskal-Wallis Test 956
18.5 Rank Correlation 961
Summary 966
Glossary 966
Key Formulas 967
Supplementary Exercises 968
Case Problem: RainOrShine.Com 971
Appendix 18.1 Nonparametric Methods with JMP 972
Appendix 18.2 Nonparametric Methods with Excel 979
Appendix 18.3 Nonparametric Methods with R (MindTap Reader)
Chapter 19 Decision Analysis 981
Statistics in Practice: Ohio Edison Company 982
19.1 Problem Formulation 983
Payoff Tables 983
Decision Trees 984
19.2 Decision Making with Probabilities 985
Expected Value Approach 985
Expected Value of Perfect Information 987
19.3 Decision Analysis with Sample Information 992
Decision Tree 993
Decision Strategy 994
Expected Value of Sample Information 998
19.4 Computing Branch Probabilities Using Bayes’ Theorem 1002
Summary 1006
Glossary 1007
Key Formulas 1008
Supplementary Exercises 1008
Case Problem 1: Lawsuit Defense Strategy 1010
Case Problem 2: Property Purchase Strategy 1011
Chapter 20 Index Numbers 1013
Statistics in Practice: U.S. Department of Labor, Bureau
of Labor Statistics 1014
20.1 Price Relatives 1014
20.2 Aggregate Price Indexes 1015
20.3 Computing an Aggregate Price Index from Price Relatives 1019
20.4 Some Important Price Indexes 1021
Consumer Price Index 1021
Producer Price Index 1021
Dow Jones Averages 1022
20.5 Deflating a Series by Price Indexes 1023
20.6 Price Indexes: Other Considerations 1026
Selection of Items 1026
Selection of a Base Period 1026
Quality Changes 1027
20.7 Quantity Indexes 1027
Summary 1029
Glossary 1029
Key Formulas 1029
Supplementary Exercises 1030
Chapter 21 Statistical Methods for Quality Control 1033
Statistics in Practice: Dow Chemical Company 1034
21.1 Philosophies and Frameworks 1035
Malcolm Baldrige National Quality Award 1036
ISO 9000 1036
Six Sigma 1036
Quality in the Service Sector 1038
21.2 Statistical Process Control 1039
Control Charts 1040
x Chart: Process Mean and Standard Deviation Known 1041
x Chart: Process Mean and Standard Deviation Unknown 1043
R Chart 1045
p Chart 1046
np Chart 1049
Interpretation of Control Charts 1049
21.3 Acceptance Sampling 1052
KALI, Inc.: An Example of Acceptance Sampling 1053
Computing the Probability of Accepting a Lot 1054
Selecting an Acceptance Sampling Plan 1056
Multiple Sampling Plans 1057
Summary 1059
Glossary 1060
Key Formulas 1060
Supplementary Exercises 1061
Appendix 21.1 Control Charts with JMP 1064
Appendix 21.2 Control Charts with R (MindTap Reader)
Chapter 22 Sample Survey (MindTap Reader) 22-1
Statistics in Practice: Duke Energy 22-2
22.1 Terminology Used in Sample Surveys 22-2
22.2 Types of Surveys and Sampling Methods 22-3
22.3 Survey Errors 22-5
Nonsampling Error 22-5
Sampling Error 22-5
22.4 Simple Random Sampling 22-6
Population Mean 22-6
Population Total 22-7
Population Proportion 22-8
Determining the Sample Size 22-9
22.5 Stratified Simple Random Sampling 22-12
Population Mean 22-12
Population Total 22-14
Population Proportion 22-15
Determining the Sample Size 22-16
22.6 Cluster Sampling 22-21
Population Mean 22-23
Population Total 22-25
Population Proportion 22-25
Determining the Sample Size 22-27
22.7 Systematic Sampling 22-29
Summary 22-29
Glossary 22-30
Key Formulas 22-30
Supplementary Exercises 22-34
Case Problem: Medicament’s Predicament 22-36
Appendix A References and Bibliography 1068
Appendix B Tables 1070
Appendix C Summation Notation 1097
Appendix D _Answers to Even-Numbered Exercises (MindTap Reader)
Appendix E _Microsoft Excel 2016 and Tools for Statistical Analysis 1099
Appendix F Computing p-Values with JMP and Excel 1107
Index 1111